Shina Sheen

@psgtech.edu

Associate Professor Department of Applied Mathematics and Computational Sciences
PSG College of Technology

Shina Sheen


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https://researchid.co/shinasheen

RESEARCH INTERESTS

Malware Detection, Network Security, Data Mining
21

Scopus Publications

Scopus Publications

  • 3D-Printed Microfluidic-Integrated SERS Salivary Biosensor Utilizing Fe@Ag/Carbon Nanofibers for Advanced Machine Learning-Driven Noninvasive, Label-Free Mass Screening of Lung Cancer
    Navami Sunil, Rajesh Unnathpadi, Rajkumar Kottayasamy Seenivasagam, Abhijith T, Latha R, Shina Sheen, K.Chandra Devi, Biji Pullithadathil
    ACS Applied Nano Materials, 2025
    Developing an advanced, noninvasive diagnostic tool for early-stage lung cancer screening is critical to enable prediagnosis and improve patient outcomes. Herein, a microfluidic surface-enhanced Raman scattering (SERS) biosensor platform fabricated using three-dimensional (3D) printing has been developed to facilitate noninvasive, point-of-care lung cancer diagnostics by integrating carbon nanofibers (CNFs) anchored with Iron@Silver (Fe@Ag) core–shell nanoparticles. The evaluation of the SERS performance using Rhodamine 6G demonstrated a significant surface enhancement factor (EF) of approximately 107, with an ultralow detection limit down to 10–12 M, affirming its superior sensitivity. The plasmonic hotspot exhibited an electric field intensity enhancement factor (|E|2/|E0|2) exceeding 600, induced at the vicinity of Fe@Ag nanoparticles, which was identified as a major factor contributing to the superior performance of the developed sensors. The integration of Fe@Ag/CNFs-based SERS substrates into the microfluidic platform addresses challenges associated with xerostomia, a common condition in lung cancer patients that limits saliva production, by enabling effective analysis of low-volume saliva samples with improved reproducibility and statistical robustness, enabling high-throughput, real-time detection of lung cancer biomarkers from saliva samples. To improve diagnostic precision, machine learning techniques have been utilized to distinguish the salivary SERS profiles of individuals with lung cancer (n = 44) from those of healthy controls (n = 45). To improve diagnostic accuracy, machine learning techniques were utilized to distinguish the salivary SERS signals of lung cancer groups (n = 44) from those of healthy individuals (n = 45). Principal Component Analysis has been used to reduce the data dimensionality, followed by application of a support vector machine classifier, achieving a classification accuracy of 94%, with a sensitivity of 93.5% and a specificity of 88%. Combining SERS with machine learning techniques underscores the promise of this microfluidic biosensor as a noninvasive and reliable tool for the early detection of lung cancer, paving the way for more accurate and efficient clinical diagnostics.
  • Development of an AI-derived, non-invasive, label-free 3D-printed microfluidic SERS biosensor platform utilizing Cu@Ag/carbon nanofibers for the detection of salivary biomarkers in mass screening of oral cancer
    Navami Sunil, Rajesh Unnathpadi, Rajkumar Kottayasamy Seenivasagam, T. Abhijith, R. Latha, Shina Sheen, Biji Pullithadathil
    Journal of Materials Chemistry B, 2025
    An AI-driven non-invasive and label-free 3D-printed SERS biosensor based on Cu@Ag integrated carbon nanofibers for detection of salivary biomarkers has been developed for mass screening, early diagnosis, and improving treatment efficacy of oral cancer.
  • Preface
    Communications in Computer and Information Science, 2025
  • Dynamic deception-based technique for crypto-ransomware detection
    Kiruthika Jagannathan, Shina Sheen
    International Journal of Security and Networks, 2024
  • Focused Crawler Based on Reinforcement Learning and Decaying Epsilon-Greedy Exploration Policy
    Parisa Begum Kaleel, Shina Sheen
    International Arab Journal of Information Technology, 2023
    In order to serve a diversified user base with a range of purposes, general search engines offer search results for a wide variety of topics and material categories on the internet. While Focused Crawlers (FC) deliver more specialized and targeted results inside particular domains or verticals, general search engines give a wider coverage of the web. For a vertical search engine, the performance of a focused crawler is extremely important, and several ways of improvement are applied. We propose an intelligent, focused crawler which uses Reinforcement Learning (RL) to prioritize the hyperlinks for long-term profit. Our implementation differs from other RL based works by encouraging learning at an early stage using a decaying ϵ-greedy policy to select the next link and hence enables the crawler to use the experience gained to improve its performance with more relevant pages. With an increase in the infertility rate all over the world, searching for information regarding the issues and details about artificial reproduction treatments available is in need by many people. Hence, we have considered infertility domain as a case study and collected web pages from scratch. We compare the performance of crawling tasks following ϵ-greedy and decaying ϵ-greedy policies. Experimental results show that crawlers following a decaying ϵ-greedy policy demonstrate better performance
  • R-Sentry: Deception based ransomware detection using file access patterns
    Shina Sheen, K A Asmitha, Sridhar Venkatesan
    Computers and Electrical Engineering, 2022
  • Evaluation and Recommendation of Fertility Hospitals Through Multi Criteria Decision Making and Rank Correlation
    K. Parisa Begum, Shina Sheen
    Communications in Computer and Information Science, 2022
  • Early Detection of Android Locker Ransomware Through Foreground Activity Analysis
    Shina Sheen, S. Gayathri
    Lecture Notes in Electrical Engineering, 2022
  • Darknet Traffic Analysis and Classification Using Numerical AGM and Mean Shift Clustering Algorithm
    R. Niranjana, V. Anil Kumar, Shina Sheen
    SN Computer Science, 2020
    The cyberspace continues to evolve more complex than ever anticipated, and same is the case with security dynamics there. As our dependence on cyberspace is increasing day-by-day, regular and systematic monitoring of cyberspace security has become very essential. A darknet is one such monitoring framework for deducing malicious activities and the attack patterns in the cyberspace. Darknet traffic is the spurious traffic observed in the empty address space, i.e., a set of globally valid Internet Protocol (IP) addresses which are not assigned to any hosts or devices. In an ideal secure network system, no traffic is expected to arrive on such a darknet IP space. However, in reality, noticeable amount of traffic is observed in this space primarily due to the Internet wide malicious activities, attacks and sometimes due to the network level misconfigurations. Analyzing such traffic and finding distinct attack patterns present in them can be a potential mechanism to infer the attack trends in the real network. In this paper, the existing Basic and Extended AGgregate and Mode (AGM) data formats for darknet traffic analysis is studied and an efficient 29-tuple Numerical AGM data format suitable for analyzing the source IP address validated TCP connections (three-way handshake) is proposed to find attack patterns in this traffic using Mean Shift clustering algorithm. Analyzing the patterns detected from the clusters results in providing the traces of various attacks such as Mirai bot, SQL attack, and brute force. Analyzing the source IP validated TCP, darknet traffic is a potential technique in Cyber security to find the attack trends in the network.
  • Ransomware detection by mining API call usage
    Shina Sheen, Ashwitha Yadav
    2018 International Conference on Advances in Computing Communications and Informatics Icacci 2018, 2018
    In the recent past one of the harmful forms of malware seen is the Ransomware. The year 2016 has seen a huge rise in ransomware attacks. According to the study by Tripwire, Ransomware has done the most amount of damage to organizations in 2017, followed by DDoS, Malicious Insiders, Phishing, and Known/Unknown Vulnerabilities. In this work, Application Programming Interface (API) calls are extracted from the executables and the most discriminating API calls are used to train a classifier to detect unknown ransomware. We have tested our method on various classifiers like Decision trees, KNN, Random forest. Class imbalance due to the difference in the number of samples available in two classes - Ransomware and benign is also considered. It is seen that Random forest with smote for class imbalance has given a detection rate of over 98%. A large number of ransomware samples have been analyzed and the discriminating API calls have been identified.
  • Preface
    Communications in Computer and Information Science, 2016
  • Computational intelligence, cyber security and computational models: Proceedings of ICC3 2015
    Advances in Intelligent Systems and Computing, 2016
  • Multilevel analysis to detect covert social botnet in multimedia social networks
    V. Natarajan, S. Sheen, R. Anitha
    Computer Journal, 2015
  • Malware detection in android files based on multiple levels of learning and diverse data sources
    Shina Sheen, Anitha Ramalingam
    ACM International Conference Proceeding Series, 2015
  • Android based malware detection using a multifeature collaborative decision fusion approach
    Shina Sheen, R. Anitha, V. Natarajan
    Neurocomputing, 2015
  • Comparative study of two-and multi-class-classification-based detection of malicious executables using soft computing techniques on exhaustive feature set
    Shina Sheen, R. Karthik, R. Anitha
    Advances in Intelligent Systems and Computing, 2014
  • Malware detection by pruning of parallel ensembles using harmony search
    Shina Sheen, R. Anitha, P. Sirisha
    Pattern Recognition Letters, 2013
  • Detection of StegoBot: A covert social network botnet
    V. Natarajan, Shina Sheen, R. Anitha
    ACM International Conference Proceeding Series, 2012
  • A novel node splitting criteria for decision trees based on Theil index
    Shina Sheen, R. Anitha
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
  • Ensemble pruning using Harmony search
    Shina Sheen, S. V. Aishwarya, R. Anitha, S. V. Raghavan, S. M. Bhaskar
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
  • Network intrusion detection using feature selection and decision tree classifier
    Shina Sheen, R. Rajesh
    IEEE Region 10 Annual International Conference Proceedings TENCON, 2008